The self-optimization (SO) model can be considered as the third operational mode of the classical Hopfield network, leveraging the power of associative memory to enhance optimization performance. Moreover, it has been argued to express characteristics of minimal agency, which renders it useful for the study of Artificial Life. In this article, we draw attention to another facet of the SO model: its capacity for creativity. Drawing on creativity studies, we argue that the model satisfies the necessary and sufficient conditions of a creative process. Moreover, we show that learning is needed to find creative outcomes above chance probability. Furthermore, we demonstrate that modifying the learning parameters in the SO model gives rise to four different regimes that can account for both creative products and inconclusive outcomes, thus providing a framework for studying and understanding the emergence of creative behaviors in artificial systems that learn.

The two-dimensional Turing machine is a promising but under used simulation tool for Artificial Life. Single-state 2-D Turing machines exhibit a variety of interesting behaviors, some of which have already been explored. Multistate 2-D Turing machines, despite their potential for simulating even more diverse behaviors, have received little attention to date. We demonstrate the potential of such automata for studying biological phenomena by showing how they can be used to simulate self-similar growth, the spread of disease, and self-reproduction. Some of the results presented here are from investigations that were performed around the time of Dewdney (1989), but they have not been published until now.

With the recent Nobel Prize awarded for radical advances in protein discovery, foundation models (FMs) for exploring large combinatorial spaces promise to revolutionize many scientific fields. Artificial Life (ALife) has not yet integrated FMs, thus presenting a major opportunity for the field to alleviate the historical burden of relying chiefly on manual design and trial and error to discover the configurations of lifelike simulations. This article presents, for the first time, a successful realization of this opportunity using vision-language FMs. The proposed approach, called automated search for Artificial Life (ASAL), (a) finds simulations that produce target phenomena, (b) discovers simulations that generate temporally open-ended novelty, and (c) illuminates an entire space of interestingly diverse simulations. Because of the generality of FMs, ASAL works effectively across a diverse range of ALife substrates, including Boids, Particle Life, the Game of Life, Lenia, and neural cellular automata. A major result highlighting the potential of this technique is the discovery of previously unseen Lenia and Boids life-forms, as well as cellular automata that are open-ended like Conway's Game of Life. Additionally, the use of FMs allows for the quantification of previously qualitative phenomena in a human-aligned way. This new paradigm promises to accelerate ALife research beyond what is possible through human ingenuity alone.

This article presents Benefit Game 2.0, a multiscreen Artificial Life gameplay installation. Saccharina latissima, a seaweed species economically beneficial to humans but threatened by overexploitation, motivates the creation of this artwork. Technically, the authors create an underwater virtual ecosystem consisting of a seaweed swarm and symbiotic fungi, created using procedural content generation via machine learning and rule-based methods. Moreover, the work features a unique cybernetic loop structure, incorporating audience observation and game token interactions. This virtual system is also symbolically influenced in real time by indoor carbon dioxide measurements, serving as an artistic metaphor for the broader impacts of climate change. This integration with the physical game machine underscores the fragile relationship between human activities and the environment under severe global climate change and immerses the audience in the challenging balance between sustainability and profit seeking in this context.

An analysis of the language we use in scientific practice is critical to developing more rigorous and sound methodologies. This article argues that how certain methods of description are commonly employed in cognitive science risks obscuring important features of an agent's cognition. We propose to make explicit a method of description whereby the concept of cognitive distinctions is the core principle. A model of referential communication is developed and analyzed as a platform to compare methods of description. We demonstrate that cognitive distinctions, realized in a graph theoretic formalism, better describe the behavior and perspective of a simple model agent than other, less systematic or natural language-dependent methods. We then consider how different descriptions relate to one another in the broader methodological framework of minimally cognitive behavior. Finally, we explore the consequences of, and challenges for, cognitive distinctions as a useful concept and method in the tool kit of cognitive scientists.

Robot controllers are often optimized for a single robot in a single environment. This approach proves brittle, as such a controller will often fail to produce sensible behavior for a new morphology or environment. In comparison, animal gaits are robust and versatile. By observing animals, and attempting to extract general principles of locomotion from their movement, we aim to design a single, decentralized controller applicable to diverse morphologies and environments. The controller implements the three components of (a) undulation, (b) peristalsis, and (c) leg motion, which we believe are the essential elements in most animal gaits. This work is a first step toward a general controller. Accordingly, the controller has been evaluated on a limited range of simulated centipede-like robot morphologies. The centipede is chosen as inspiration because it moves using both body contractions and legged locomotion. For a controller to work in qualitatively different settings, it must also be able to exhibit qualitatively different behaviors. We find that six different modes of locomotion emerge from our controller in response to environmental and morphological changes. We also find that different parts of the centipede model can exhibit different modes of locomotion, simultaneously, based on local morphological features. This controller can potentially aid in the design or evolution of robots, by quickly testing the potential of a morphology, or be used to get insights about underlying locomotion principles in the centipede.

Central to the Artificial Life endeavor is the creation of artificial systems that spontaneously generate properties found in the living world, such as autopoiesis, self-replication, evolution, and open-endedness. Though numerous models and paradigms have been proposed, cellular automata (CA) have taken a very important place in the field, notably because they enable the study of phenomena like self-reproduction and autopoiesis. Continuous CA like Lenia have been shown to produce lifelike patterns reminiscent, from both aesthetic and ontological points of view, of biological organisms we call "creatures." We propose Flow-Lenia, a mass conservative extension of Lenia. We present experiments demonstrating its effectiveness in generating spatially localized patterns with complex behaviors and show that the update rule parameters can be optimized to generate complex creatures showing behaviors of interest. Furthermore, we show that Flow-Lenia allows us to embed the parameters of the model, defining the properties of the emerging patterns, within its own dynamics, thus allowing for multispecies simulation. Using the evolutionary activity framework and other metrics, we shed light on the emergent evolutionary dynamics taking place in this system.

PCs are complicated. Yet, being generally more effective, they have replaced typewriters in everyday life. Because of their complications, many of us wonder at PCs as if they were mysterious ghosts in the machine: entities with powers we cannot explain or control, almost supernatural. I analyze how this increase in technological complication may be limiting our society at two levels, one economic and one scientific, and I discuss how the field of Artificial Life (ALife) can attempt to rescue it. At the economic level, there is evidence that computers, being complicated, slow labor productivity rather than increasing it (e.g., maintenance, malware, distractions). Computers are also the subject of debate surrounding technological unemployment and elite overproduction. I advocate for ALife to focus on minimally intrusive developments to our everyday work and to occupy unfilled economic niches, like xenobots or bacterial biofilms. At the scientific level, the surge in artificial intelligence has resulted in many complex algorithms that mimic the cognition happening in brains: Even their creators struggle to make sense of them. I advocate for ALife to focus more on basal forms of cognition, cognition that requires as little "brain" as possible, potentially none-algorithms that think through their bodies, stripped of any superfluous complications, just like typewriters. Ultimately, my goal is for the reader to ask themselves what values should drive ALife.